Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning
Key Features
- Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset
- Acquire implementable knowledge on digital transformation through 8 practical laws
- Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly
Book Description
In today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator.
The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise.
The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning.
By the end of the book, you will have the tools and techniques to drive your organization's digital transformation.
Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book:
Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon.
What you will learn
- Train your organization to transition from being data-driven to being value-driven
- Navigate and master the big data business model maturity index
- Learn a methodology for determining the economic value of your data and analytics
- Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used
- Become aware of digital transformation misconceptions and pitfalls
- Create empowered and dynamic teams that fuel your organization's digital transformation
Who this book is for
This book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.
Table of Contents
- The CEO Mandate: Become Value-driven, Not Data-driven
- Value Engineering: The Secret Sauce for Data Science Success
- A Review of Basic Economic Concepts
- University of San Francisco Economic Value of Data Research Paper
- The Economic Value of Data Theorems
- The Economics of Artificial Intelligence
- The Schmarzo Economic Digital Asset Valuation Theorem
- The 8 Laws of Digital Transformation
- Creating a Culture of Innovation Through Empowerment
- Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics
- Appendix B: The Economics of Data, Analytics, and Digital Transformation Cheat Sheet
Bill Schmarzo, The Dean of Big Data is a University of San Francisco School of Management Executive Fellow and an Honorary Professor at the School of Business and Economics at the National University of Ireland-Galway where he teaches and mentors students in his courses "Big Data MBA" and "Thinking Like a Data Scientist". He is the author of Big Data: Understanding How Data Powers Big Business, Big Data MBA: Driving Business Strategies with Data Science, and The Art of Thinking Like a Data Scientist. He has written countless whitepapers, articles and blogs, and given keynote presentations and university lectures on the topics of data science, artificial intelligence/machine learning, data economics, design thinking and team empowerment.
